Optimization methods for glycerin refining process

Abstract

This invention discloses an optimization method for a glycerol refining process, comprising the following steps: Based on the adopted glycerol refining process, determining the control factors, and conducting single-factor experiments with each control factor as a variable to obtain the optimized first control factor. The control factor is selected from at least one of the following: the ratio of silica gel to zeolite powder, the amount of decolorizing agent added, and the decolorization temperature; then conducting an orthogonal experimental design; setting an evaluation subset and a comment set; setting a weight set; determining a fuzzy relation comprehensive evaluation set; establishing and analyzing a quadratic response surface regression model; using software to analyze the fitted equation to obtain the optimal process parameters for glycerol refining, and using a fuzzy mathematical sensory evaluation model to verify and determine the optimal process parameters for glycerol refining. This invention uses fuzzy mathematical evaluation combined with response surface methodology to examine the effects of the ratio of silica gel to zeolite powder, the amount of decolorizing agent added, and the decolorization temperature on the glycerol content, thus obtaining the optimal refining process.

Description

Technical Field

[0001] This invention belongs to the field of glycerin refining technology, specifically relating to an optimization method for glycerin refining process. Background Technology

[0002] High-concentration glycerin is a non-toxic and safe substance with wide applications in cosmetics and food, and its applications are constantly expanding. In the food processing industry, glycerin is commonly used as a preservative, sweetener, and humectant in sports foods and milk substitutes, serving a lubricating and nourishing function while providing a refreshing sensation.

[0003] There are many purification processes for glycerol. The commonly used purification and refining process is vacuum distillation. However, vacuum distillation requires high distillation temperature and high energy consumption. High temperature can easily cause glycerol to decompose or polymerize to generate new products, and the quality of the obtained glycerol cannot be guaranteed. Moreover, there are drawbacks such as large workload and lack of precision in process optimization.

[0004] The information disclosed in this background section is intended only to enhance the understanding of the overall background of the invention and should not be construed as an admission or in any way implying that the information constitutes prior art known to those skilled in the art. Summary of the Invention

[0005] The purpose of this invention is to provide an optimization method for glycerol refining process, which combines fuzzy mathematical evaluation and response surface methodology to obtain the optimal glycerol refining process.

[0006] To achieve the above objectives, a specific embodiment of the present invention provides the following technical solution:

[0007] An optimized method for glycerol refining process includes the following steps:

[0008] Based on the glycerol refining process adopted, control factors are determined, and single-factor experiments are conducted on each control factor as a variable to obtain the optimized first control factor. The control factor is selected from at least one of the following: the ratio of silica gel to zeolite powder, the amount of decolorizing agent added, and the decolorization temperature.

[0009] The glycerin refining process includes:

[0010] S1. Mix zeolite powder and silica gel in a certain proportion to make an adsorbent;

[0011] S2. The crude glycerol is adsorbed through an adsorbent to obtain the initial product;

[0012] S3. Pass the initial product through an ion exchange resin column to obtain a semi-finished product;

[0013] S4. Add the decolorizing agent to the semi-finished product for decolorization, then filter and dry to obtain the finished product.

[0014] In one or more embodiments of the present invention: it further includes conducting a multi-factor, multi-level response surface methodology based on the first control factor to obtain orthogonal experimental results, and thereby obtaining an optimized second control factor.

[0015] In one or more embodiments of the present invention: the orthogonal experiment results are evaluated based on the second control factor to obtain an optimal third control factor, wherein the evaluation process includes:

[0016] Set up evaluation subsets and comment sets: Use the color, odor, transparency, and viscosity of glycerin as evaluation indicators. The evaluation indicators form an evaluation subset U, U = (U1, U2, U3, U4) = (color, odor, transparency, viscosity); evaluate the grade of refined glycerin, and the comment set for each evaluation indicator is represented by V, V = (poor, good, excellent).

[0017] Set weight set: Assign weights to the evaluation subset to obtain the weight set K;

[0018] Determine the fuzzy relation comprehensive evaluation set: Assess each evaluation indicator of refined glycerin by dividing the number of votes for each indicator in each comment by the total number of evaluators to obtain the fuzzy evaluation matrix a; process the comprehensive evaluation results of glycerin using fuzzy mathematics methods; based on the principle of fuzzy matrix transformation, multiply the weight set K by the fuzzy evaluation matrix a to obtain the evaluation result y of glycerin; set scores according to sensory quality levels to obtain the level matrix R; the product of the level matrix R and the evaluation result y is the fuzzy comprehensive score T;

[0019] Establishment and analysis of the quadratic response surface regression model: The ratio of silica gel to zeolite powder (A), the amount of decolorizing agent added (B), and the decolorization temperature (C) were used as response surface factors, and the glycerol content (Y) was used as the response value. The response results were fitted by multiple regression using software to obtain the quadratic response surface equation, and variance and significance analysis were performed.

[0020] The optimal process parameters for glycerol refining were obtained by using software analysis and fitting equations. The optimal process parameters for glycerol refining were then verified using a fuzzy mathematical sensory evaluation model, thus obtaining an optimized glycerol refining process.

[0021] In one or more embodiments of the present invention, sensory quality is set as excellent, good, and poor, with scores of 100, 90, and 80 respectively, and the rating matrix R = (100, 90, 80).

[0022] In one or more embodiments of the present invention, the weight set K = (color, odor, transparency, viscosity) = (0.35, 0.30, 0.25, 0.10).

[0023] In one or more embodiments of the present invention, the quadratic response surface equation is Y(%) = 99.27 + 1.56A + 0.87B + 0.33C + 0.84AB + 1.29AC - 1.35BC - 11.06A 2 -6.22B 2 -3.6C 2 .

[0024] In one or more embodiments of the present invention, in step S1, the mass ratio of silica gel to zeolite powder is (0.5 to 2.5):1.

[0025] In one or more embodiments of the present invention, in step S4, the decolorizing agent includes rice husk charcoal, quartz sand and activated carbon.

[0026] In one or more embodiments of the present invention, in step S4, the amount of decolorizing agent added is 0.5 to 2.5% of the mass of the semi-finished product.

[0027] In one or more embodiments of the present invention, in step S4, the decolorization temperature is 60-100°C.

[0028] Compared with existing technologies, this invention uses crude glycerol as the main raw material. Based on single-factor experiments, the adsorbent ratio, decolorizing agent dosage, and decolorization temperature were selected as independent variables. Fuzzy mathematics combined with response surface methodology was used to investigate the effects on glycerol content. The optimal glycerol refining parameters were determined to be an adsorbent silica gel to zeolite ratio of 1.5:1, a decolorizing agent dosage of 1.0%, and a decolorization temperature of 80℃. Under these conditions, the model mathematically predicted a glycerol content of 99.36. Multiple repeated experiments yielded a glycerol content of (99.55±0.07), which showed no significant difference from the theoretical value, fully verifying the correctness of the regression model and demonstrating the reasonable design of the response surface methodology. Attached Figure Description

[0029] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0030] Figure 1 This invention provides a comparison of the effects of different ratios of silica gel and zeolite on glycerol content.

[0031] Figure 2 This invention provides a comparison of the effects of the amount of decolorizing agent added on the glycerol content.

[0032] Figure 3This invention provides a comparison of the effects of decolorization temperature and dosage on glycerol content.

[0033] Figure 4 The response surface of the effect of the ratio of silica gel to zeolite and the amount of decolorizing agent added on the glycerol content in this invention;

[0034] Figure 5 The contour lines represent the effects of the ratio of silica gel to zeolite and the amount of decolorizing agent added on the glycerol content in this invention.

[0035] Figure 6 The response surface of the effect of the ratio of silica gel to zeolite and the decolorization temperature on the glycerol content in this invention;

[0036] Figure 7 The contour lines represent the effects of the silica gel to zeolite ratio and decolorization temperature on the glycerol content in this invention.

[0037] Figure 8 The response surface of the effect of the amount of decolorizing agent and the decolorization temperature on the glycerol content in this invention;

[0038] Figure 9 The contour lines represent the effects of the amount of decolorizing agent and the decolorization temperature on the glycerol content in this invention. Detailed Implementation

[0039] To enable those skilled in the art to better understand the technical solutions of this invention, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this invention.

[0040] An optimized method for glycerol refining process includes steps S1-S1074.

[0041] S1, Adsorbent Preparation: Zeolite was pulverized and soaked in a 10% hydrochloric acid solution for 2 hours. It was then washed with water until neutral, and subsequently calcined and dried in a muffle furnace at 300°C. The pulverized zeolite was then passed through a 200-mesh sieve. Silica gel with a particle size of 74–154 μm was mixed with the activated zeolite in different proportions to prepare the adsorbent for later use.

[0042] S2, Adsorption: Different proportions of adsorbent were packed into the column in sequence, and crude glycerol was passed through the column to adsorb impurities in the crude glycerol. The effluent was collected for later use. The purity of the crude glycerol was 72%, and it was purchased from Qingdao Jinniu Oil Technology Co., Ltd.

[0043] S3, Resin Secondary Adsorption: Dilute the effluent with 1 volume of pure water and pass it through D201 strong basic ion exchange resin at a flow rate of 2.2 ml / min; take the effluent passed through D201 strong basic ion exchange resin and pass it through 732 strong acid ion exchange resin at a flow rate of 1.5 ml / min, and take the effluent.

[0044] S4, Decolorizing agent: Wash rice husk charcoal, dry it, crush it into powder, add water and stir. Pass the slurry through an 18-mesh sieve to remove impurities and large particles of rice husk charcoal, and dry it for later use to obtain product A; Take an appropriate amount of quartz sand and soak it in a 5% sulfuric acid solution for 2 hours to clean and activate it. After drying, pass it through a 100-mesh sieve to obtain product B; Take activated carbon that has passed through a 200-mesh sieve and mix products A, B and activated carbon in a mass ratio of 1:1:2 to obtain the decolorizing agent.

[0045] S5, Decolorization: Decolorize the effluent by changing the decolorization temperature and the amount of decolorizing agent added (i.e., the mass ratio of the decolorizing agent to the effluent), and take the decolorized glycerol effluent.

[0046] S6, Drying: Take the decolorized glycerin eluent and remove the moisture from the glycerin.

[0047] S7, Testing: The glycerol content is tested using conventional testing methods, and sensory tests are performed on the glycerol odor, color, and transparency.

[0048] S8, Single-factor experiment:

[0049] S81. Under the conditions of using 100.0 g of crude glycerol raw material, a decolorization temperature of 80℃, and a decolorizing agent addition of 1.0%, the effects of silica gel to zeolite mass ratios of 0.5:1, 1:1, 1.5:1, 2:1, and 2.5:1 on the color, filtration efficiency, and glycerol content of the glycerol product were investigated. The experimental results are as follows: Figure 1 As shown, Figure 1 The horizontal axis represents the mass ratio of silica gel to zeolite, and the vertical axis represents the average glycerol content (%).

[0050] Depend on Figure 1 It can be seen that different adsorbent ratios have different effects on glycerol content. The glycerol content shows a trend of first increasing and then decreasing. When the mass ratio of silica gel to zeolite is 1.5:1, the color of the effluent is light yellow, the adsorption effect of impurities is high, and the glycerol content also reaches the highest value. Therefore, three levels of silica gel to zeolite mass ratios of 1:1, 1.5:1, and 2:1 were selected for subsequent response surface experiments.

[0051] S82. Under the conditions of 100.0 g of ion-exchanged glycerol raw material, a decolorization temperature of 80℃, and an adsorbent ratio of 1.5:1, the effects of adding 0.5%, 1.0%, 1.5%, 2.0%, and 2.5% of the decolorizing agent on the color, filtration, and glycerol content of the glycerol product were investigated. The experimental results are as follows: Figure 2 As shown;

[0052] Depend on Figure 2 As can be seen, different amounts of decolorizing agent have different effects on glycerol content. The glycerol content shows a trend of first increasing and then decreasing. This is because the greater the amount of decolorizing agent added, the greater the loss of glycerol. When 0.5% of decolorizing agent is added, the product color is darker and the filtration effect is poor. Therefore, based on its filtration effect, the amount of decolorizing agent is determined to be 1.0%. At this time, the product color is light yellow and the filtration effect is relatively easy. Therefore, three levels of decolorizing agent addition of 0.5%, 1.0%, and 1.5% are selected for subsequent response surface experiments.

[0053] S83, under the conditions of 100.0 g of ion-exchanged glycerol raw material, 1.0% decolorizing agent, and an adsorbent ratio of 1.5:1, the effects of decolorizing temperatures of 60℃, 70℃, 80℃, 90℃, and 100℃ on the color, filtration, and glycerol content of the glycerol product were investigated. The experimental results are as follows: Figure 3 As shown;

[0054] Depend on Figure 3 As can be seen, different decolorization temperatures have different effects on glycerol content. The glycerol content first increases and then decreases with increasing temperature. This is because a suitable temperature can improve the decolorization and adsorption effect of the decolorizing agent, increasing the removal rate and adsorption capacity of the adsorbent. However, excessively high temperatures can cause sintering and carbonization, affecting its surface and pore structure and degrading the adsorption performance of the adsorbent. At decolorization temperatures of 60℃ and 70℃, the filtration effect is relatively difficult. Therefore, considering both glycerol content and filtration effect, the decolorization temperature was determined to be 80℃. At this temperature, the product is colorless, the filtration effect is relatively easy, and the glycerol content is also relatively high. Therefore, three levels of decolorization temperature, namely 75℃, 80℃, and 85℃, were selected for subsequent response surface methodology experiments.

[0055] S9, Orthogonal Experimental Design: Based on the results of single-factor experiments, a three-factor, three-level response surface methodology was designed, selecting three factors: adsorbent ratio (A), decolorizing agent dosage (B), and decolorization temperature (C). Adsorbent ratio, decolorizing agent dosage, and decolorization temperature were used as independent variables, and glycerol content as the dependent variable. The optimal process parameters for glycerol content were determined using Design-Expert 13 software. The specific factor levels for the experimental design are shown in Table 1.

[0056] Table 1. Experimental Design of Factor Levels in Response Surface Analysis

[0057]

[0058]

[0059] S10, Establish a fuzzy mathematical evaluation model:

[0060] A panel of 10 healthy food professionals aged 25-45, who do not smoke or drink alcohol, was selected and trained in sensory evaluation of oils related to glycerin. The color, odor, transparency, and viscosity of glycerin were then evaluated, and the average results were taken. The evaluation criteria are shown in Table 2.

[0061] Table 2 Sensory Evaluation Table for Glycerin

[0062]

[0063] S101, Determination of evaluation objects: The set of evaluation objects is the set of glycerols to be evaluated, X = (X1, X2, X3, ..., X17), denoted by Xi, where i = 1, 2, 3, ..., 17;

[0064] S102, Evaluation factors are determined: four sensory indicators of glycerin are used as factors, namely U = (U1, U2, U3, U4), where U1 represents the color of glycerin, U2 represents the odor of glycerin, U3 represents the transparency of glycerin, and U4 represents the viscosity of glycerin.

[0065] S103, Determination of the evaluation grade set: The evaluation team members evaluate the grade of the refined glycerin and determine the evaluation grade set as V = (poor v1, good v2, excellent v3) = (80 points, 90 points, 100 points).

[0066] S104, Determining the evaluation weights:

[0067] The weight and influence of each factor in the evaluation factors are called the weights. According to the importance of each indicator in the evaluation, the higher the importance, the greater the weight. The set of evaluation objects is represented by K = (k1, k2, k3, k4), and k1 + k2 + k3 + k4 = 1. In this invention, 10 evaluators are selected to score the importance of four quality factors of refined glycerin, namely color, odor, transparency and viscosity. The total score is 10 points. The ratio of the sum of the scores of each factor to the total score of 100 points is the weight factor of each factor.

[0068] Table 3 shows the statistical results of the weighting of four quality factors of refined glycerin by 10 sensory evaluators. As can be seen from Table 3, there are certain differences in the weighting of the four influencing factors such as color, odor, transparency and viscosity among the sensory evaluators. The weight score of each influencing factor is divided by the total score of 100 to obtain the weight of each quality factor, namely color (0.35), odor (0.30), transparency (0.25) and viscosity (0.10). Among them, color and odor have larger weights, while transparency and viscosity have smaller weights.

[0069] Table 3. Weighting of Glycerin Evaluation Factors

[0070]

[0071]

[0072] S105, Rewards and Results of Fuzzy Matrix:

[0073] Ten evaluators evaluated 17 groups of refined glycerin in an orthogonal experimental design. As shown in Table 4, among the 10 evaluators, the number of votes for excellent, good, and poor in the color evaluation of sample 1 were 0, 1, and 9, respectively. The fuzzy evaluation matrix a1 of sample 1 was obtained by dividing the number of votes for each grade of different indicators of sample 1 by the total number of evaluators.

[0074] Table 4 Glycerin Rating Table

[0075]

[0076]

[0077] Based on the results in Table 4, the data in the table is converted into a fuzzy mathematical matrix:

[0078]

[0079] S106, Calculation and analysis of fuzzy mathematical sensory rating:

[0080] The product of the weight matrix K and the fuzzy relation matrix a is the comprehensive evaluation of the sample;

[0081] Taking sample 1 as an example, the product of the weight matrix K and the fuzzy relation matrix a1 is the comprehensive evaluation y1 of sample 1, i.e., y1 = K·a1. Similarly, the sum evaluation results of other samples can be obtained.

[0082]

[0083] S107, Box-Behnken experimental design and results analysis:

[0084] The sensory quality is set as excellent, good, and poor, with scores of 100, 90, and 80 respectively. The product of the grade matrix R = (100, 90, 80) and the evaluation result y is the fuzzy comprehensive score T.

[0085] Taking sample 1 as an example, T1 = y1 × R = (0, 0.065, 0.935) × (100, 90, 80) = 80.65. Similarly, the comprehensive scores of other samples can be obtained. See Table 5 for details.

[0086] Table 5. Experimental Design Results of Box-Behnken Response Surface Methodology

[0087]

[0088]

[0089] S1071, Establishment and Analysis of a Quadratic Response Surface Regression Model:

[0090] Based on the above experiments, the adsorbent ratio (A), decolorizing agent dosage (B), and decolorization temperature (C) were used as response surface factors, and the glycerol content was used as the response value (Y). Origin-Expert 13 software was used to perform multiple regression fitting on the response results to estimate the glycerol content. Terms with insignificant effects on the model were removed, and the following quadratic response surface equation was obtained after optimization:

[0091] Y(%)=99.27+1.56A+0.87B+0.33C+0.84AB+1.29AC-1.35BC-11.06A 2 -

[0092] 6.22B 2 -3.6C 2 .

[0093] S1072, Box-Behnken Experiment ANOVA:

[0094] Table 6. Significance Tests and Analysis of Variance for the Regression Equation Model

[0095] factor sum of squares Degrees of freedom Mean Square F value p-value Significance Model 841.70 9 93.52 105.95 <0.0001 ** A - Adsorbent ratio 19.53 1 19.53 22.13 0.0022 ** B-Decolorizing Agent Addition Amount 6.04 1 6.04 6.84 0.0346 * C - Decolorization temperature 0.8778 1 0.8778 0.9945 0.3519 / AB 2.81 1 2.81 3.18 0.1178 / AC 6.63 1 6.63 7.51 0.0289 * BC 7.29 1 7.29 8.26 0.0239 * <![CDATA[A 2 ]]> 515.05 1 515.05 583.52 <0.0001 ** <![CDATA[B 2 ]]> 163.03 1 163.03 184.70 <0.0001 ** <![CDATA[C 2 ]]> 54.49 1 54.49 61.74 0.0001 ** residual 6.18 7 0.8827 / / / Mismatch 4.78 3 1.59 4.54 0.0890 Not significant Pure error 1.40 4 0.3508 / / / sum 847.87 16 / / / /

[0096] Note: ** indicates extremely significant difference (P<0.01), * indicates significant difference (P<0.05);

[0097] Each experiment was conducted in triplicate, and their mean values ​​were calculated and expressed as mean ± standard deviation. Origin 2018 and Excel software were used to process the results, and Design-Expert 13 software was used to analyze and calculate the one-way variance. The significance criteria were p < 0.05 for significant difference and p < 0.01 for highly significant difference.

[0098] The variance and significance of the regression model were analyzed, and the results are shown in Table 6. Table 6 shows that the p-value of the model is <0.0001, indicating that the model is highly significant and the experimental method is reliable. The lack-of-fit term has F=4.54 and p=0.0890>0.05, indicating that the difference is not significant, suggesting that the experimental model has a high degree of fit with the experimental results. The R-value of the model is [not specified]. 2 =0.9927, Corrected coefficient of determination R adj 2 =0.9833, indicating that the regression model closely matches the experimental data and can well reflect the true experimental value. The p-value shows that the interaction terms (AC, BC) and the linear term (B) have a significant impact on glycerol content (P < 0.05), while the linear term (A) and the quadratic term (A) have a significant impact. 2 B 2 C 2 The effect of ) on sensory evaluation was extremely significant (P < 0.01), while the effects of other items on the results were not significant (P > 0.05);

[0099] The F-values ​​indicate that the order of influence of the three factors on the glycerol content is: adsorbent ratio (A) > decolorizing agent addition (B) > decolorizing agent decolorization temperature (C).

[0100] S1073, Response surface and contour analysis results:

[0101] Figure 4-9 The trend graph of the three factors of adsorbent ratio, decolorizing agent addition amount, and decolorization temperature with glycerol content as the response value is mapped to the bottom to form a contour line, indicating the influence of each pair of factors on the response value and the strength of their interaction; an elliptical contour indicates a large interaction, while a circular contour indicates a small interaction, which is consistent with the conclusion of the regression equation.

[0102] S1074, Verification of experimental results:

[0103] Using Design-Expert 13 to analyze the fitted equation, the optimal glycerol refining parameters were determined to be an adsorbent ratio of 1.54:1, a decolorizing agent addition of 1.03%, and a decolorization temperature of 80.23℃. Under these conditions, the model's sensory score prediction was 99.37 points. To verify the accuracy of the model's prediction, and for ease of practical operation, the process parameters were adjusted to an adsorbent ratio of 1.5:1, a decolorizing agent addition of 1%, and a decolorization temperature of 80℃ for multiple repeated experiments. The glycerol content obtained was (99.56±0.07)%, which showed no significant difference from the theoretical value, fully validating the correctness of the regression model and demonstrating the reasonableness of the response surface methodology design.

[0104] In summary, this invention first combines a specific adsorbent, ion exchange method, and decolorizing agent to form a process route for refining crude glycerol, obtaining high-purity glycerol. Secondly, this invention uses fuzzy mathematical evaluation combined with response surface methodology to predict the glycerol content, obtaining the optimal process route. Specifically, the adsorbent ratio (silica gel:zeolite) is 1.5:1, the decolorizing agent addition is 1.0%, and the decolorization temperature is 80℃. Under these conditions, the glycerol content is (99.55±0.07)%, with a small deviation from the theoretical prediction. Response surface methodology for optimizing the glycerol refining process has advantages such as simple operation, precise conditions, and intuitive results; the established regression equation can analyze the influence of various factors on the indicators and can be used for practical prediction.

[0105] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, it is intended that all variations falling within the meaning and scope of equivalents of the claims be included within the present invention.

[0106] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. An optimized method for a glycerin refining process, characterized in that, Includes the following steps: Based on the glycerol refining process adopted, control factors are determined, and single-factor experiments are conducted on each control factor as a variable to obtain the optimized first control factor. The control factor is selected from at least one of the following: the ratio of silica gel to zeolite powder, the amount of decolorizing agent added, and the decolorization temperature. The glycerin refining process includes: S1. Mix zeolite powder and silica gel in a certain proportion to make an adsorbent; S2. The crude glycerol is adsorbed through an adsorbent to obtain the initial product; S3. Pass the initial product through an ion exchange resin column to obtain a semi-finished product; S4. Add the decolorizing agent to the semi-finished product for decolorization, then filter and dry to obtain the finished product; In step S1, the mass ratio of silica gel to zeolite powder is 5:

1.

2. The optimized method for the glycerol refining process according to claim 1, characterized in that: It also includes designing a multi-factor, multi-level response surface methodology based on the first control factor to obtain orthogonal experimental results, and thereby obtaining an optimized second control factor.

3. The optimized method for the glycerol refining process according to claim 2, characterized in that: It also includes evaluating the orthogonal experiment results based on the second control factor to obtain the optimal third control factor, the evaluation process including: Set up evaluation subsets and comment sets: Use the color, odor, transparency, and viscosity of glycerin as evaluation indicators. The evaluation indicators form an evaluation subset U, U = (U1, U2, U3, U4) = (color, odor, transparency, viscosity); evaluate the grade of refined glycerin, and the comment set for each evaluation indicator is represented by V, V = (poor, good, excellent). Set weight set: Assign weights to the evaluation subset to obtain the weight set K; Determine the fuzzy relation comprehensive evaluation set: Assess each evaluation indicator of refined glycerin by dividing the number of votes for each indicator in each comment by the total number of evaluators to obtain the fuzzy evaluation matrix a; process the comprehensive evaluation results of glycerin using fuzzy mathematics methods; based on the principle of fuzzy matrix transformation, multiply the weight set K by the fuzzy evaluation matrix a to obtain the evaluation result y of glycerin; set scores according to sensory quality levels to obtain the level matrix R; the product of the level matrix R and the evaluation result y is the fuzzy comprehensive score T; Establishment and analysis of the quadratic response surface regression model: The ratio of silica gel to zeolite powder (A), the amount of decolorizing agent added (B), and the decolorization temperature (C) were used as response surface factors, and the glycerol content (Y) was used as the response value. The response results were fitted by multiple regression using software to obtain the quadratic response surface equation, and variance and significance analysis were performed. The optimal process parameters for glycerol refining were obtained by using software analysis and fitting equations. The optimal process parameters for glycerol refining were then verified using a fuzzy mathematical sensory evaluation model, thus obtaining an optimized glycerol refining process.

4. The optimized method for the glycerol refining process according to claim 3, characterized in that, Sensory quality is set as excellent, good, and poor, with scores of 100, 90, and 80 respectively, and the rating matrix R = (100, 90, 80).

5. The optimized method for the glycerol refining process according to claim 3, characterized in that, The weight set K = (color, odor, transparency, viscosity) = (0.35, 0.30, 0.25, 0.10).

6. The optimized method for the glycerol refining process according to claim 3, characterized in that, The quadratic response surface equation is Y(%) = 99.27 + 1.56A + 0.87B + 0.33C + 0.84AB + 1.29AC - 1.35BC - 11.06A 2 -6.22B 2 -3.6C 2 .

7. The optimized method for the glycerol refining process according to claim 1, characterized in that, In step S4, the decolorizing agent includes rice husk charcoal, quartz sand, and activated carbon.

8. The optimized method for the glycerol refining process according to claim 1, characterized in that, In step S4, the amount of decolorizing agent added is 0.5 to 2.5% of the mass of the semi-finished product.

9. The optimized method for the glycerol refining process according to claim 1, characterized in that, In step S4, the decolorization temperature is 60–100°C.

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